Identification and Inference for Algorithmic Frontiers with Selective Labels
This paper introduces a method for identifying and inferring the fairness-accuracy frontier, a concept crucial in econometrics. The proposed techniques allow for hypothesis testing and the construction of confidence sets for this frontier, particularly when outcome data is only available for a subset of individuals. The research provides a characterization of the identification region for the FA-frontier under specific selection processes and loss measurements, with extensions to broader loss functions currently in progress. AI